Predicting Software Reliability with Support Vector Machines
Jung-Hua Lo · 2010
Support vector machine (SVM) is a new method based on statistical learning theory. It has been successfully used to solve nonlinear regression and time series problems. However, SVM has rarely been applied to software reliability prediction. In this study, an SVM-based model for software reliability forecasting is proposed. In addition, the parameters of SVM are determined by Genetic Algorithm (GA). Empirical results show that the proposed model is more precise in its reliability prediction and is less dependent on the size of failure data comparing with the other forecasting models.